Research And Development Of News Recommendation System Based On GIS

In recent years,the hot spots of the times have shifted from the era of Internet big data to data filtering and cleaning.One of the most prominent problems exposed by the rapid development of Internet technology is the exponential increase in the amount of information.For ordinary users,a large amount of unscreened information is rushing forward,and it becomes increasingly difficult for users to screen and collect the information they need.At present,user-oriented personalized recommendation services have been widely used on major platform websites to prevent users from falling into the fog of information.However,the recommendation algorithm still needs to continue to be optimized to achieve efficient recommendation.In summary,how to find the high-quality information that people need in the massive data has become a hot research problem.Geospatial location is one of the indispensable factors in the information society,and it is also one of the key information collected by the Internet industry for research on user data.The current news recommendation system recommends only the surrounding news information based on positioning,and lacks relevant analysis based on the user itself and the push news in terms of information screening,and lacks news academic information for the GIS industry.Based on the above background,this article makes a detailed analysis and research on news acquisition,screening,and recommendation algorithms.This research analyzes the current research status and development of the current news recommendation system and recommendation algorithm,and proposes that the future development of the recommendation system will combine different recommendation algorithms with each other,and use auxiliary algorithms for news location information recommendation scenarios to improve recommendation accuracy.This paper studies the existing recommendation methods such as content-based recommendation,collaborative filtering recommendation,and hybrid recommendation algorithm,and analyzes the technical principles,application fields,and their respective advantages and disadvantages.Based on GIS spatial analysis method,using K-means algorithm,Geohash coding method combined with collaborative filtering recommendation algorithm,clustering news keyword features,screening and associating location-related news and users,to achieve more accurate recommendation and location information Recommend a more reasonable effect.The GIS-based news recommendation system demand analysis,system design and system implementation are carried out on the recommendation method studied in this paper.In the end of this research,the GIS-based news recommendation system is unit tested,stress tested,and Web page displayed,and the clustering results and Geohash coding improvement recommendation success rate are statistically analyzed.